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Recent years have seen a dramatic change in the data processing landscape. Before, data centers processed data in batches; it was collected over time and then processed all at once.
Fremont, CA: In real-time data processing, data is handled as it arrives, so it can be used almost immediately after it is collected and created. In business data centers, this term refers to the ability to take data and make informed decisions based on it as quickly as possible.
Furthermore, predictive analytics, artificial intelligence (AI), and machine learning (ML) rely on a well-designed and operational real-time data processing system.
As companies have begun to adapt to the problems of today's data world, many have created a strategy for using real-time data processing to shape their organizations.
Current Data Processing Challenges
Recent years have seen a dramatic change in the data processing landscape. Before, data centers processed data in batches; it was collected over time and then processed all at once. This method works well when data isn't time-sensitive. Still, as business demands have changed and data has gotten more complicated, real-time data processing has become critical for many firms.
The major issue right now is scalability. Businesses must be able to grow real-time resources efficiently while also boosting revenue. Several factors, however, make this challenging.
• Massive data expansion
Scaling has become difficult due to the emergence of big data. Data centers must be able to handle data fast and effectively as they collect more data than ever before. Global data generation is expected to surpass 180 zettabytes by 2025, according to a Statista report. The present data processing environment, on the other hand, will not be able to accommodate this expansion.
• Increased digitization
Another difficulty is the digitalization of data and procedures. As more data is created in digital formats, current systems are strained, making real-time processing more challenging. Again, it's due to the fact that digital data must frequently get translated into a format that machines can understand. As a result, companies are fast discovering that they need to invest in additional on-premises or cloud-based solutions.
• Real-time analytics
The requirement for real-time analytics necessitates real-time data processing. Businesses must be able to examine data in near real-time in order to make swift choices. This requires a strategy distinct from batch data processing.
Instead of processing data all at once, it should get done as it is collected. The issue is that the available tools are relatively new, requiring users to go through a high learning curve. Furthermore, they are frequently fairly costly.